Allocation of Resources and Scheduling in Cloud ...

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 9, Number 19 (2014) pp. 5827-5837 © Research India Publications http://www.ripublication.com

Allocation of Resources and Scheduling in Cloud Computing with Cloud Migration 1

K.Radha 2 B.Thirumala Rao 3 Shaik Masthan Babu 4K.Thirupathi Rao 5 V.Krishna Reddy 6P.Saikiran 1

Research Scholar, Dept.of CSE, KL University Assistant Professor, Sri Sai Educational Societies 2, 4, 5, 6 Professor, Dept.of CSE, KL University 1 [email protected] [email protected] 3 [email protected]

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Abstract Resource management and allocation of resources is major problem in cloud computing environment. Due to less cloud infrastructures cloud users are facing the problems to select the suitable cloud service provider. This paper presents about the resource allocation and management of resources scheduling in cloud computing and optimizing the price for provisioning of resources in cloud computing. Capacity allocation algorithm is proposed for multi tier systems in a distributed environment. Profit optimization is a problem in a Multi-dimensional allocation of resources to the singledimensional applications in the distributed systems. Force-directed resource assignment solving the problem of optimization. Initial solution provides the solution to gain the profit upper bound problem. Index Terms— Resource Allocation, Profit Optimization, Initial Solution, Capacity allocation, Force-directed resource allocation.

INTRODUCTION TO SERVICE BROKERING IN INTERCLOUD ENVIRONMENTS WITH SLA A divergent and low cloud infrastructure is a problem for cloud consumers when selecting their suitable cloud service provider and it is tied to a specific cloud provider. Intercloud computing allows users to easily transfer the cloud user’s application workloads via clouds irrespective of cloud service provider platform. Cloud service broker identifies the suitable cloud service provider by satisfying the cloud user service needs. Architecture for cloud service broker is proposed to operate

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in inter cloud environment. Service brokers are defines as trusted advisors who makes possible business deal. An emerging operational role is introduced by Cloud service brokerage, where IT aggregates the services from numerous cloud service providers. The service broker upholds the customer by choosing the service provider that should satisfy the customer needs. Proposed approach is a generic architecture for a Cloud service broker operating in an Intercloud environment. Cloud service broker has three advantages such as Service broker allows consistent access to cloud resources through a standardized Cloud abstraction layer between customers and service providers, controlling the deployed cloud service hosted on various cloud service providers, acquiring the suitable cloud resources specified by SLA. Cloud service broker contains three components such as Cloud Broker, Cloud exchange and Cloud Coordinator. Client initiated the cloud broker to meet the QoS. Cloud Coordinator acting as a mediator between their internal datacenters and external cloud. Cloud Exchange acts as intermediary between the consumer and service provider. Cloud Computing has technology for outsourcing enterprises and IT infrastructures on financial basis which allows dynamic Purveying of virtual hardware as per their needs in a pay-per–pattern. In previous years, vendor-lock in and scarcity challenges block the interoperability via cloud service providers. Now–a- day’s choosing the appropriate cloud offers that satisfies the needs of consumers is a challenging problem. Cloud service broker architecture’s Components are as follows SLA Manager, Controlling and Finding the Manager, Match Maker, Deployment Manager, Identity Manager, Persistence and intercloud Gateway. To find out the SLA features supported by various Cloud providers, User asks that with the help of mediator, SLA manager access the Service provider SLA template published through intercloud gateways. Negotiation process monitored by the service broker to meet an SLA agreement among the users and suitable cloud service providers. SLA negotiation process as follows, user post a service request 1. With SLA to SLA manager, then 2.SLA manager asks the Match maker that if it executes the service specific requirements to respond to this request, 3. Match maker begins a match making process to discover suitable provider, 4. By matching the collected resource properties from controlling manager with service requirements. Finally user gets the response against his request from SLA manager with corresponding matching results.5 after receiving the response agreement is established with matched provider and needed resources could be reserved. If any of the service providers does not match then above mentioned steps will be repeated for renegotiation until meeting the agreement. Provisioning and Monitoring of SLA established after an agreement, request is posted by the user to SLA Manager. SLA provision is follows after receiving the request SLA manager translates the associated SLA to service request and asks the deployment Manager to deploy service [1].

ALLOCATION OF RESOURCES FOR MULTI-TIER DISTRIBUTED SYSTEMS WITH MULTI-DIMENSIONAL SLA Distributed systems have growth in demand for computing and memory. Allocation of Resources is the main issue in a cloud computing systems whenever end-users

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consists service level agreements. Resource allocation in SLA is a problem for multiple applications are considered in the cloud computing. To solve this problem force directed search algorithm is proposed which contains an upper bound on the maximum benefit is provided. Optimization is implemented in Processing, demand for the memory and dispatch of resources [2].Compare to single tier applications, multi tier applications are difficult. IT Infrastructure’s datacenter operators should achieve the SLAs (Service Level Agreements) with the customers. Service level Agreements contains storage space, security, availability, etc. Client applications are compute-intensive and memory-intensive. There are two different kinds of SLA agreements such as Gold SLA class, bronze SLA class. In the Gold SLA class, if the constraint is violated then the response time is guaranteed and cloud service provider pays the fine amount. In the bronze SLA class, each customer can have specific utilization function based upon it’s acknowledge. Massive datacenter owners are geographically distributed to decrease the need of information containers on the local power framework which gives highest defect toleration and reduced cost of ownership. In massive computing systems, efficiency of energy can be improved through the allocation of resource consolidation and server consolidation. An Analytical model for the web based services are determines the median of response time for resources based on the median analysis [4]. Multi-dimensional allocation of resource for single step applications in the distributed systems presents a SLA based model on application’s acknowledgement is taken into account to construct the profit optimization problem [5]. Solution is local optimization techniques and generating initial solution. A virtualization management scheme manages the performance, effectualness and strength of a server system [7]. Dynamic resource management reduces the operational price of system and improves the strength of the resources. For massive number of servers and application sizes Dynamic resource provisioning technique is used [6]. to monitor the resources for each module Resource provisioning allows the service providers. Multi-dimensional assignment of resource policy provides transmission of resources for multiple applications that acquire the massive inter-server communications. Resource management problem maximizes the total profit for providing service to the consumers. Profit maximization is a difficult issue and it consumes more time to provide the solution for linear relaxation of system parameters. A solution for profit maximization is Force-directed resource assignment. Solution for the profit upper bound problem Initial solution. Resource consolidation technique is derived from force–directed scheduling and it is a scheduling technique and it consolidates the resources and determines the active servers to minimize the resource assignment [8]. Resource allocation and multiple Service Level Agreements (SLA) parameters are taken into consideration for the high priority task execution improves the utilization of resource in distributed environment. SLA parameters are CPU time, bandwidth and memory. Resource allocation approach is proposed for the multi-dimensional resource allocation problem to execute user’s applications. For static allocation of resources, adaptive list scheduling is used [28]. Allocation of resource model of distributed environment uses an optimal joint multiple resource allocation method, optimal resource allocation method. Allocated resources are committed to each service

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request. These methods are decrease the probability of requisition loss and reduce the total resource. Cloud service providers desire to increase the income through high utilization of resource and cloud users reduce the expenditure to reach the performance needs of cloud users. It is tedious to assign the resources in mutual manner because of scarcity of information shared among the cloud users and service providers [29]. There are several resources in massive computational infrastructure bandwidth, CPU load and operating systems. To provide the quality of service resources are allocated to user’s applications through load balancing mechanism. To increase the utilization of cloud optimized distributed infrastructure devices should be maintained. Cloud service provider tasks are combines for assigning and utilizing the insufficient resources within the limitation of distributed environments to satisfy the requirements of cloud user in Resource Allocation Strategy (RAS). Resource Allocation Strategy needs kind of resources by every application to finish the user task. Resource Allocation Strategy avoids when applications are accessing the same resource at the same time. Dynamic Resource Allocation Research Issues are as follows, Dynamic resource allocation for Spot Markets in Clouds, Dynamic Optimization of Multi-Attribute, allocation of resource in self-organizing clouds, heterogeneity-Aware Resource Allocation and Scheduling in cloud, Allocation of Resource using Virtual Machines for Cloud Computing Environment, allocation of resource strategies in Cloud Computing, Priority based allocation of resource and scheduling in cloud, Heuristic based Allocation of resource using virtual machine migration, A Dynamic Allocation of Resource Methods for parallel data processing, Efficient Idle Desktop Consolidation with Partial Virtual Machine Migration, A Cloud Computing Perspective. First data processing approach for dynamic resource allocation is called Nephele, It is offered by today’s Infrastructure as Service for task scheduling and execution. An efficient resource allocation approach will get the maximum benefit. Efficient resource allocation approach is an issue, and also customer satisfaction and server’s performance can’t be maintained equally. Resources of virtual machine between cloud applications cost is reduced by multi-dimensional resource allocation approach with some nodes to process the application [31]. In Dynamic resource allocation approach based up on the load of Virtual machines on infrastructure as a service users are dynamically add and delete according to the constraints given by the user [32]. Dynamic resource allocation for the management of virtual machine is an issue to achieve without interruption managing multiple virtualization platforms and multiple virtual machine migration via physical machines. Dynamic resource allocation deals with virtualization machines on physical machines, which results in, Virtual machines are automatically shifted to less loaded physical machine without service interruption [33]. Prediction-based dynamic allocation of resource approach is allocated and deallocates virtual machines to group of video transcoding servers in a horizontal manner. Prediction-based dynamic resource allocation approach is used to allocate and deallocate the virtual machines to group of video transcoding servers in a horizontal manner. Two-way Load prediction method permits proactive allocation of resource with high accuracy under real-time constraints [34].

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INITIAL SOLUTION The structure of allocating the resources to the clients gives the quality of solution specifically when the maximum computational resources should reach the cloud user’s needs. To predict the structure for allocation of resource process, greedy technique is implemented to rate the client’s application tiers for every client. Clients are ordered in increasing order and processing is done from lower to higher metric value which permits to allocate the resources to clients that require many resources. Selected clients tiers are arranged in homogeneous fashion. After choosing the enduser for the allocation of resources, the needed tiers for the client is organized on the basis of simulation of server availability is multiplied by rating the service of server to the specified server from the lowest to highest metric. After choosing the client and tier, some of the servers are already in enabled state and they have selected the tier and some servers are not allocated the resources. Considering these modifications, servers are allocated to other tiers and they are deleted from resource repository. Solution to this problem is upper bound profit solution.

MANAGEMENT OF RESOURCES AND SCHEDULING IN A DISTRIBUTED ENVIRONMENT Consumers can retrieve the applications and information or data of the cloud from anywhere and anytime. Such that it will be more difficult to the cloud service providers for allocating the resources of cloud dynamically and efficiently [9]. Allocating resources dynamically in a distributed environment is a complicated task because of tedious process of allocating the similar resources to identical PCs. Hence allocation of resources is an issue in cloud computing [10]. To efficiently assign the computing resources, Scheduling becomes a tedious in distributed systems where more substitutable computers may vary with available capacities. In Distributed systems the execution process need resource management because of heavy procedure to the ratio of resource. Resource Scheduling is difficult tasks in distributed environment due to more number of PCs which varies with capacities. Job-oriented resource scheduling in distributed environment is proposed to effectively manage and schedule the resources. Job-oriented scheduling algorithms achieve high performance computation and better throughput. Conventional algorithms are not providing scheduling in distributed environments. Online Mode Heuristic scheduling Algorithms, Batch model Heuristic Algorithms (BMHA) is Job–oriented scheduling algorithms. Jobs are in the form of queue when they arrive into the system. Allocation of resource is done based upon the availability of resources and user priorities. Computational resources are allocated as per the rank of the job. Shortest Remaining Time First algorithms (SRTF), Round Robin, Pre-emptive Priority are the resource scheduling algorithms for the analysis purpose. SRTF is the well-organized algorithm for resource scheduling and it has lowest time parameters [3]. A cloud service provider gets many computational requests with various requirements and priorities from users [11]. Multi-variable SLA monitors the resource scheduling for application supplying on the cloud [12], [13]. Consigning the pool of cloud resources from various cloud service providers that satisfies the end–user’s

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application with minimal best effort of service. In [14], Balakrishna and Somasundaram proposed a framework for evaluating the availability of resources in the environment and develop schemes for each resource. Due to massive number of various kinds of service level agreements, computational resources are facing the challenges. Solution to this problem is restriction of various kinds of resources to a small number of computational resources. These computational resources are determined across SLA templates. A SLA template specifies the SLA construction, service elements and names of service elements. This approach can never change in consumer requirements. To provide solution to this approach Adaptive SLA mapping is provided. This scheme is taking the SLA template based upon the SLA matching of end-users [15]. Resource Management is a major issue in distributed environment, because system resources continuously allocated to handle the workloads distractions meanwhile service level agreements are provided to cloud users. Capacity allocation algorithms are proposed to cooperate multiple dispense resource controllers operate in a geographical dispensed cloud sites [21]. Capacity allocation provides a load redirection which assigns incoming requests between various sites and also minimizes the price of allocation of resources. Web based services hosted by more than one site of a framework of a cloud provider. A deployed service provides resource demands, workload fluctuations and SLA needs. Resource Allocation allocates the available resources for the required cloud based applications via internet. To maintain the resources for every module cloud service providers permitting the allocation of resources. A resource allocation strategy combines the cloud service provider tasks to use and assign the lacking of resources in the distributed environment to reach requirements of Cloud applications. Resource allocation strategy meets customer satisfaction and profit maximization for the cloud providers [22]. To improve the reliability and performance of resource in data centers load balancing plan for resource scheduling is to be applied. Online Load balance Resource Scheduling Algorithm is proposed for cloud data centers in multidimensional resources. Conventional load balance scheduling algorithms does not contain the static intervals. Online Load balance resource scheduling algorithm (OLRSA) considers life cycles and static intervals for the physical and virtual machines. OLRSA has efficient performance. Now-a-days cloud data centers contain the Virtualization technology supports on-demand allocation. Cloud Data centers move from one set of resources to another set of resources. Virtualization migration and Virtualization enables cloud data center to enhance the computational resources and to utilize the optimum physical servers. Virtual Migration provides server consolidation, online maintenance, load balancing and proactive fault tolerance. Virtual migration violates the service level agreement which will results in performance degradation. Virtualization improves the utilization of resource. Resource allocation algorithms will take requirements of resources to change the resources which are allocated. Data center are consuming more energy in cooling and power distribution. Dynamic capacity provision is used to reduce the energy utilization to map the requirements of resources by modifying the active machines to achieve Service level objectives of workloads and delay in job scheduling. Resource allocation scheme (RAS) is categorized into two types such as core nodes and

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accelerator nodes and RAS reduces the cost. Cloud environment consists of two users such as consumers and service providers. Cloud service providers task is to increase the revenue through utilizing the resources. Cloud users task is to reduce the cost to reach their performance requirements. Allocating and scheduling computational resources to the problem of cloud resource management such that service providers meet the excessive resource utilization and cloud users achieve their performance needs of applications with minimum investment. For the problem of cloud resource management, topology-aware resource placement solution is proposed to avoid the scarcity of sharing the information among the cloud providers and cloud users, at provider’s side. Allocation of resource strategy to monitor the collection of leased resources in an expensive manner and progress share-based job scheduling algorithm results in high performance in diverse distributed environment. Resource Scheduling with user’s QoS demand and to reach maximum benefit is the problem, to address this problem; genetic algorithm is used to construct the resource utilization of cloud to optimize the allocation of resource. To meet efficiency in cloud computing Task scheduling is a primary challenge. Task scheduling is an issue to design an efficient algorithm. For the good results bandwidth-aware algorithm for divisible task scheduling. To achieve the good performance, bandwidth-aware algorithm for divisible task scheduling is proposed in distributed environment [23]. Resource allocation and scheduling system manages the resources of cloud provider’s and monitor the client’s requests to QoS and to reduce the cloud provider’s cost. Resource management monitors the new user requests for virtual machine which are located on these servers. Resource allocation paradigms can be categorized into three ways namely, processing resources of data center, network resources of data center and energy-efficient resource allocation approach. Processing resources increases the computational throughput of data center and it scatters virtual machines to reduce the distance between their places in data center grid [24]. Architecture of resource management with the cooperation of computing systems via numerous virtual machines increases the performance of computational systems will improve the utilization of resources designed for on-demand utilization of resource. Architecture of resource management has benefits of some components in virtualized platform, cloud computing platform and grid computing platform reduces the overhead of computational systems. Architecture of resource management with the cooperation of computing systems has highest CPU utilization and best performance. Resource management contains assignment of resources, monitoring the resources, resource planning, resource deployment, load management and resource cycle [26].

OPTIMIZING THE RESOURCE PROVISIONING COST IN A DISTRIBUTED ENVIRONMENT Resource Purveying [16] for distributed environment is a major issue that how the resources allocating service level agreements to cloud users to satisfy the user needs. Performance paradigm contains two classes to predict the smallest number of servers is needed to fulfill the SLA for both the classes. There are two allocation policies such as Shared allocation and dedicated allocation. In Distributed environment cloud

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service providers provides two plans such as for cloud users reservation and ondemand plan. Compared to the utilization cost of computational resources is lower than on-demand plan. Resources reservation is typical to achieve due to end-users future request and service provider’s resource cost. Optimal Cloud resource Provisioning algorithm is proposed to solve this problem. This algorithm is used for multiple provisioning phases along with long-term plan. First–come First Serve or Processor Sharing is used for the profit minimization of SLA. It takes the response time to schedule the decision such as heuristic Cost-Based scheduling [19]. Robust resource Provisioning algorithm is used to achieve the better reservation. Robust Cloud Resource Provisioning minimizes the total provisioning cost by taking into consideration that demand, price and resources utilization. Cloud computing permits the users to scale-up and scale-down the business user’s usage of resources based upon their requirements. Resource allocation and multiple Service Level Agreements (SLA) parameters are taken into consideration for the high priority task execution improves the utilization of resource in distributed environment. SLA parameters are CPU time, bandwidth and memory. Resource provisioning in distributed environment is to achieve the load balancing. Virtual machine based resource provisioning is adopted in distributed environments. Allocations of virtual machines by using static scheduling mechanism, resources are not completely utilized. In efficient cloud resource provisioning approach, software as a service takes resources from cloud service providers to Saas users. Saas provider’s purpose is to reduce the amount of using Virtual machines from cloud service providers and increases benefit via Saas requests. Saas provider reaches Quality of Service and meets the need of cloud users. Cloud service provider increases the profit without overstep upper bound of energy usage of cloud service provider for purveying the virtual machines to Saas provider. Saas user’s aim is to get the minimized QoS to finish their tasks within the limited budget and time period. Optimal cloud resource provision algorithm provision the computational resources are used in multiple provisioning phases. Virtual machine uses optimal cloud resource provisioning in dynamic resource allocation. Computational task are distributed in cloud computing on the collection of resources which contains the large scale computers to get the maximum computational strength. Cloud platform deploys the services and applications for users and business to access the computation as a service. There are resource allocation scheduling problems in cloud computing. One of the problems is virtual machine provisioning to resources.

CONCLUSION This paper is concluding that, the problem of allocation of resources to minimize the total benefit got from the SLA contracts and lost from operational cost. Multi-tier applications guarantee the SLA to achieve profit in the distributed environment. Force-directed allocation of resources approach is proposed to get the optimization of profit. A shortest Remaining Time First algorithm (SRTF) is the most efficient algorithm for resource scheduling and it has lowest time parameters. Capacity allocation provides a load redirection which assigns incoming requests between

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various sites and also minimizes the price of allocation of resources. Cloud computing permits business users to scale-up and scale-down the business user’s resources are used based upon their requirements.

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